One of Australia's leading technology companies needed to feed their in-house AI models with clean, real-time, consent-aware behavioural data - across 6 markets, web, app and backend services. We architected and delivered one of the most complex Tealium rollouts in APAC, built specifically to integrate with their proprietary AI infrastructure.
Replace a fragmented GTM + Segment implementation with a single, governed, real-time data backbone - and integrate it natively with the client's AI inference layer.
Bi-directional: events stream from Tealium into the AI stack in under a second, and model outputs flow back into AudienceStream as live attributes used for activation.
Six privacy regimes, zero-tolerance for downtime and a production AI stack already dependent on the legacy data pipe during the entire migration window.
Single source of truth for client-side tagging across web and app. Consolidated 40+ vendor tags behind a normalised data layer with strict naming conventions and a versioned extension library.
Server-side event collection from web, mobile SDKs and backend services. Real-time fan-out to vendor endpoints, the client's AI inference layer, BigQuery and Snowflake - with per-destination transformations.
Real-time visitor stitching across devices and channels. Audience attributes computed on the fly from 80+ enrichments, then fed back into the AI model and out to activation endpoints.
Bi-directional connector between Tealium and the client's in-house AI stack - sub-second event delivery in, model scores and predicted segments back out into AudienceStream as live attributes.
OneTrust integrated with Tealium Consent Manager. Per-region purpose mapping (GDPR, CCPA, Australian Privacy Act), consent state propagated server-side to every downstream destination.
Custom Datadog dashboards on EventStream throughput, vendor delivery success, schema-validation failures and AI-pipeline lag. PagerDuty alerts on anomaly detection per event class.
Audited every existing tag, event and destination. Designed a normalised, versioned data layer with a strict JSON schema, mapped to the AI team's feature requirements.
Stood up Tealium iQ, EventStream and AudienceStream in a sandbox profile alongside the legacy stack. Built every connector, extension and audience in code (Tealium API + Git) for reviewability.
Built the bi-directional connector to the client's AI stack - EventStream → model in, model scores → AudienceStream attributes out. Validated end-to-end latency under 800ms p95.
Ran the new pipeline in shadow alongside legacy for 3 weeks. Reconciled events at 99.97% parity before cutover. Automated regression tests on every tag deployment.
Region-by-region cutover with traffic-mirroring and instant rollback. Trained the client's analytics, AI and marketing teams on the new governance model and runbooks.
300+ events were unified behind a single governed schema. End-to-end event-to-AI latency dropped 70% (from multi-second batch to sub-second streaming). The AI team reported a 3× improvement in input data quality, directly improving model precision on key personalisation tasks. Tag governance and QA time fell 55%, and the client now ships new marketing tags and AI features in days rather than weeks - without compromising consent or compliance in any of the 6 markets.